UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views (2409.02917v2)

Abstract: Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. Despite the impressive achievements of Neural Radiance Field (NeRF), its direct application to surgical scenes produces unsatisfying results due to two challenges: endoscopic sparse views and significant photometric inconsistencies. In this paper, we propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views. The core of UC-NeRF is to incorporate the multi-view uncertainty estimation to condition the neural radiance field for modeling the severe photometric inconsistencies adaptively. Specifically, our UC-NeRF first builds a consistency learner in the form of multi-view stereo network, to establish the geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies. Furthermore, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experiments on the SCARED and Hamlyn datasets demonstrate our superior performance in rendering appearance and geometry, consistently outperforming the current state-of-the-art approaches. Our code will be released at https://github.com/wrld/UC-NeRF.

• The paper introduces a novel uncertainty-aware dual-branch NeRF that effectively tackles sparse and photometrically inconsistent endoscopic views.
• It employs a consistency learner and monocular depth distillation to refine both geometric correspondence and depth estimation.
• Experimental results on SCARED and Hamlyn datasets demonstrate improved PSNR, SSIM, and reduced depth errors compared to state-of-the-art methods.

Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views: An Expert Analysis

The paper "Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views" presents a novel approach named UC-NeRF which tackles the significant challenges in novel view synthesis within minimally invasive surgical (MIS) environments. The authors address the severe limitations of endoscopic imaging, characterized by sparse views and notable photometric inconsistencies. Their proposed method demonstrates superior performance in rendering both appearance and geometry, consistently superseding state-of-the-art methodologies.

Core Contributions and Methodology

The authors categorize their contributions into three primary dimensions:

1. Consistency Learner for Geometric Correspondence and Uncertainty Estimation: The paper introduces a consistency learner built upon the CasMVSNet architecture to resolve geometric correspondence issues from sparse endoscopic images. Leveraging multi-view stereo networks, the consistency learner estimates dense depth maps and an uncertainty map. This uncertainty estimation effectively identifies areas of photometric inconsistency, which are common in surgical scenes due to varying perspectives and lighting changes. The geometric alignment from sparse views is further refined with robust guidance from sparse SfM points, ensuring higher fidelity in downstream neural rendering tasks.
2. Uncertainty-aware Dual-branch Neural Radiance Fields: UC-NeRF employs a dual-branch NeRF architecture designed to handle the complex shape-radiance ambiguity in surgical scenes. The base branch, conditioned on the learned geometric and appearance priors, focuses on reconstructing stable and consistent aspects of the scene. The adaptive branch, weighted more heavily in regions of high uncertainty, addresses view-dependent effects and photometric changes, enabling the model to adapt dynamically to variations observed in surgical environments. This spatially adaptive strategy effectively mitigates the photometric inconsistencies and enhances the overall rendering quality.
3. Monocular Geometry Prior Distillation: To further refine the depth rendering, the authors incorporate monocular geometric priors through a two-fold distillation process. They first utilize SfM-derived sparse depth points to maintain global scale consistency. Secondly, they introduce uncertainty-guided monocular depth distillation which applies varying loss functions based on uncertainty maps, ensuring spatially adaptive supervision and regularization in depth prediction. This dual approach significantly enhances the depth estimation accuracy, especially in high-uncertainty regions.

Experimental Validation and Implications

The efficacy of UC-NeRF is validated on two datasets: SCARED and Hamlyn, which contain challenging surgical scenes replete with weak textures, reflections, and occlusions. The quantitative and qualitative results indicate substantial improvements over baseline methods, achieving higher PSNR, SSIM, and lower LPIPS and depth error metrics. Particularly, UC-NeRF demonstrates a clear advantage in handling sparse views, outperforming pre-trained and geometry-guided NeRF variants even without extensive fine-tuning.

Key Experimental Insights:

• UC-NeRF shows the ability to generalize across multiple surgical scenes, achieving superior performance without the heavy computational cost associated with fine-tuning large datasets.
• The dual-branch strategy with uncertainty-guided adaptation effectively balances robustness in geometry rendering and the fidelity of view-dependent details.
• The incorporation of monocular geometry priors further ensures the rendered geometry aligns well with real-world scales, reducing artifacts and improving the reliability of depth predictions.

Future Directions

The implications of this work are profound for the realms of surgical navigation and robotic surgery. The enhanced 3D reconstruction and novel view synthesis capabilities can significantly improve intra-operative visualizations, aiding surgeons in navigating complex anatomical landscapes. Additionally, this approach holds potential for applications in virtual reality (VR) simulations and training systems for MIS, where realistic and accurate rendering of surgical scenes is paramount.

Future research can extend this framework to accommodate highly dynamic surgical scenes, integrating temporal dimensions to capture spatial-temporal changes in real-time. Enhancing the efficiency further to support real-time applications can also broaden its applicability. Moreover, exploring more advanced neural representations and integrating them with UC-NeRF can yield even faster and more robust rendering capabilities.

Conclusion

The UC-NeRF model marks a significant step forward in overcoming the novel view synthesis challenges posed by endoscopic sparse views and photometric inconsistencies in MIS. By incorporating uncertainty-guided adaptation within a dual-branch NeRF framework, coupled with robust geometric priors, the proposed method achieves high fidelity and accuracy in both appearance and geometry rendering. This paper provides compelling evidence for its superior performance, suggesting broader implications and opening avenues for future advancements in medical imaging and AI-driven surgical assistance.

For further details, interested readers can access the code associated with this work on the provided GitHub repository.